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A robotic sensory system with high spatiotemporal resolution for texture recognition

Author

Listed:
  • Ningning Bai

    (Southern University of Science and Technology
    Xidian University)

  • Yiheng Xue

    (Southern University of Science and Technology)

  • Shuiqing Chen

    (Southern University of Science and Technology)

  • Lin Shi

    (Southern University of Science and Technology)

  • Junli Shi

    (Southern University of Science and Technology)

  • Yuan Zhang

    (Southern University of Science and Technology)

  • Xingyu Hou

    (Southern University of Science and Technology)

  • Yu Cheng

    (Southern University of Science and Technology)

  • Kaixi Huang

    (Southern University of Science and Technology)

  • Weidong Wang

    (Xidian University)

  • Jin Zhang

    (Southern University of Science and Technology)

  • Yuan Liu

    (University of Houston)

  • Chuan Fei Guo

    (Southern University of Science and Technology)

Abstract

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Although modern robots integrated with flexible sensors can precisely detect pressure, shear force, and strain, they still perform insufficiently or require multi-sensors to respond to both static and high-frequency physical stimuli during the interaction. Here, we report a real-time artificial sensory system for high-accuracy texture recognition based on a single iontronic slip-sensor, and propose a criterion—spatiotemporal resolution, to corelate the sensing performance with recognition capability. The sensor can respond to both static and dynamic stimuli (0-400 Hz) with a high spatial resolution of 15 μm in spacing and 6 μm in height, together with a high-frequency resolution of 0.02 Hz at 400 Hz, enabling high-precision discrimination of fine surface features. The sensory system integrated on a prosthetic fingertip can identify 20 different commercial textiles with a 100.0% accuracy at a fixed sliding rate and a 98.9% accuracy at random sliding rates. The sensory system is expected to help achieve subtle tactile sensation for robotics and prosthetics, and further be applied to haptic-based virtual reality and beyond.

Suggested Citation

  • Ningning Bai & Yiheng Xue & Shuiqing Chen & Lin Shi & Junli Shi & Yuan Zhang & Xingyu Hou & Yu Cheng & Kaixi Huang & Weidong Wang & Jin Zhang & Yuan Liu & Chuan Fei Guo, 2023. "A robotic sensory system with high spatiotemporal resolution for texture recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42722-4
    DOI: 10.1038/s41467-023-42722-4
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    References listed on IDEAS

    as
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    Cited by:

    1. Xiangnan He & Biao Zhang & Qingjiang Liu & Hao Chen & Jianxiang Cheng & Bingcong Jian & Hanlin Yin & Honggeng Li & Ke Duan & Jianwei Zhang & Qi Ge, 2024. "Highly conductive and stretchable nanostructured ionogels for 3D printing capacitive sensors with superior performance," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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